title: CoCiter: an efficient tool to infer gene function by assessing the significance of literature co-citation.
abstract: A routine approach to inferring functions for a gene set is by using function enrichment analysis based on GO, KEGG or other curated terms and pathways. However, such analysis requires the existence of overlapping genes between the query gene set and those annotated by GO/KEGG. Furthermore, GO/KEGG databases only maintain a very restricted vocabulary. Here, we have developed a tool called "CoCiter" based on literature co-citations to address the limitations in conventional function enrichment analysis. Co-citation analysis is widely used in ranking articles and predicting protein-protein interactions (PPIs). Our algorithm can further assess the co-citation significance of a gene set with any other user-defined gene sets, or with free terms. We show that compared with the traditional approaches, CoCiter is a more accurate and flexible function enrichment analysis method. CoCiter is freely available at www.picb.ac.cn/hanlab/cociter/.
authors.affiliation: Chinese Academy of Sciences Key Laboratory of Computational Biology, Chinese Academy of Sciences-Max Planck Partner Institute for Computational Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, China ; Center of Molecular Systems Biology, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing, China ; University of Chinese Academy of Sciences, Beijing, China.
authors.forename: Nan
authors.initials: N
authors.lastname: Qiao
authors.forename: Yi
authors.initials: Y
authors.lastname: Huang
authors.forename: Hammad
authors.initials: H
authors.lastname: Naveed
authors.forename: Christopher D
authors.initials: CD
authors.lastname: Green
authors.forename: Jing-Dong J
authors.initials: JD
authors.lastname: Han
mesh_terms: D030541:Databases, Genetic; D016247:Information Storage and Retrieval; D020407:Internet